{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "is_executing": false,
     "name": "#%% md\n"
    }
   },
   "source": [
    "# livelossplot example: Keras\n",
    "\n",
    "Last update: `livelossplot 0.5.0`. For code and documentation, see [livelossplot GitHub repository](https://github.com/stared/livelossplot).\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/stared/livelossplot/blob/master/examples/keras.ipynb\" target=\"_parent\">\n",
    "    <img src=\"https://colab.research.google.com/assets/colab-badge.svg\"/>\n",
    "</a>\n",
    "\n",
    "## Note\n",
    "\n",
    "The syntax changed, and from `0.5+` on it is (for raw Keras, and Keras as a TensorFlow module, respectively):\n",
    "\n",
    "```{python}\n",
    "from livelossplot import PlotLossesKeras, PlotPlossesKerasTF\n",
    "```\n",
    "\n",
    "and alternate API is\n",
    "\n",
    "```{python}\n",
    "from livelossplot.inputs.keras import PlotLossesCallback\n",
    "from livelossplot.inputs.tf_keras import PlotLossesCallback\n",
    "```\n",
    "\n",
    "For livelossplot 0.3 and 0.4, it is\n",
    "\n",
    "```{python}\n",
    "from livelossplot.keras import PlotLossesCallback\n",
    "from livelossplot.tf_keras import PlotLossesCallback\n",
    "```\n",
    "\n",
    "It works (for now), but will raise deprecation errors. Please update your API to the newest one.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: livelossplot in /home/bartolo/PycharmProjects/livelossplot (0.5.0)\r\nRequirement already satisfied: matplotlib in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from livelossplot) (3.0.0)\r\nRequirement already satisfied: notebook in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from livelossplot) (5.6.0)\r\nRequirement already satisfied: numpy>=1.10.0 in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from matplotlib->livelossplot) (1.15.2)\r\nRequirement already satisfied: cycler>=0.10 in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from matplotlib->livelossplot) (0.10.0)\r\nRequirement already satisfied: kiwisolver>=1.0.1 in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from matplotlib->livelossplot) (1.0.1)\r\nRequirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from matplotlib->livelossplot) (2.4.6)\r\nRequirement already satisfied: python-dateutil>=2.1 in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from matplotlib->livelossplot) (2.8.1)\r\nRequirement already satisfied: jinja2 in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from notebook->livelossplot) (2.11.1)\r\n",
      "Requirement already satisfied: nbformat in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from notebook->livelossplot) (5.0.4)\r\nRequirement already satisfied: nbconvert in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from notebook->livelossplot) (5.5.0)\r\n",
      "Requirement already satisfied: terminado>=0.8.1 in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from notebook->livelossplot) (0.8.1)\r\nRequirement already satisfied: prometheus-client in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from notebook->livelossplot) (0.7.1)\r\nRequirement already satisfied: jupyter-core>=4.4.0 in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from notebook->livelossplot) (4.5.0)\r\nRequirement already satisfied: ipython-genutils in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from notebook->livelossplot) (0.2.0)\r\nRequirement already satisfied: Send2Trash in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from notebook->livelossplot) (1.5.0)\r\nRequirement already satisfied: traitlets>=4.2.1 in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from notebook->livelossplot) (4.3.2)\r\nRequirement already satisfied: tornado>=4 in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from notebook->livelossplot) (5.1.1)\r\nRequirement already satisfied: pyzmq>=17 in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from notebook->livelossplot) (17.1.2)\r\nRequirement already satisfied: ipykernel in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from notebook->livelossplot) (4.10.0)\r\nRequirement already satisfied: jupyter-client>=5.2.0 in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from notebook->livelossplot) (5.3.3)\r\nRequirement already satisfied: six in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from cycler>=0.10->matplotlib->livelossplot) (1.14.0)\r\nRequirement already satisfied: setuptools in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from kiwisolver>=1.0.1->matplotlib->livelossplot) (46.0.0)\r\nRequirement already satisfied: MarkupSafe>=0.23 in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from jinja2->notebook->livelossplot) (1.0)\r\n",
      "Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from nbformat->notebook->livelossplot) (2.6.0)\r\nRequirement already satisfied: mistune>=0.8.1 in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from nbconvert->notebook->livelossplot) (0.8.3)\r\nRequirement already satisfied: pygments in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from nbconvert->notebook->livelossplot) (2.5.2)\r\nRequirement already satisfied: bleach in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from nbconvert->notebook->livelossplot) (3.1.0)\r\nRequirement already satisfied: defusedxml in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from nbconvert->notebook->livelossplot) (0.6.0)\r\nRequirement already satisfied: entrypoints>=0.2.2 in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from nbconvert->notebook->livelossplot) (0.3)\r\nRequirement already satisfied: pandocfilters>=1.4.1 in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from nbconvert->notebook->livelossplot) (1.4.2)\r\nRequirement already satisfied: testpath in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from nbconvert->notebook->livelossplot) (0.4.4)\r\nRequirement already satisfied: decorator in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from traitlets>=4.2.1->notebook->livelossplot) (4.4.1)\r\nRequirement already satisfied: ipython>=4.0.0 in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from ipykernel->notebook->livelossplot) (5.8.0)\r\n",
      "Requirement already satisfied: webencodings in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from bleach->nbconvert->notebook->livelossplot) (0.5.1)\r\nRequirement already satisfied: pickleshare in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from ipython>=4.0.0->ipykernel->notebook->livelossplot) (0.7.4)\r\nRequirement already satisfied: simplegeneric>0.8 in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from ipython>=4.0.0->ipykernel->notebook->livelossplot) (0.8.1)\r\nRequirement already satisfied: prompt-toolkit<2.0.0,>=1.0.4 in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from ipython>=4.0.0->ipykernel->notebook->livelossplot) (1.0.15)\r\nRequirement already satisfied: pexpect; sys_platform != \"win32\" in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from ipython>=4.0.0->ipykernel->notebook->livelossplot) (4.6.0)\r\nRequirement already satisfied: wcwidth in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython>=4.0.0->ipykernel->notebook->livelossplot) (0.1.8)\r\nRequirement already satisfied: ptyprocess>=0.5 in /home/bartolo/anaconda3/envs/livelossplot/lib/python3.5/site-packages (from pexpect; sys_platform != \"win32\"->ipython>=4.0.0->ipykernel->notebook->livelossplot) (0.6.0)\r\n",
      "\u001b[33mYou are using pip version 10.0.1, however version 20.0.2 is available.\r\nYou should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "!pip install livelossplot --quiet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from keras.datasets import mnist\n",
    "from keras.utils import to_categorical\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Flatten, Dense, Activation\n",
    "\n",
    "# raw keras\n",
    "from livelossplot import PlotLossesKeras\n",
    "\n",
    "# tensorflow.keras\n",
    "# from livelossplot import PlotLossesKerasTF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "# data loading\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "# data preprocessing\n",
    "Y_train = to_categorical(y_train)\n",
    "Y_test = to_categorical(y_test)\n",
    "X_train = X_train.reshape(-1, 28, 28, 1).astype('float32') / 255.\n",
    "X_test = X_test.reshape(-1, 28, 28, 1).astype('float32') / 255."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Flatten(input_shape=(28, 28, 1)))\n",
    "model.add(Dense(10))\n",
    "model.add(Activation('softmax'))\n",
    "\n",
    "model.compile(optimizer='rmsprop',\n",
    "              loss='categorical_crossentropy',  # 'mean_squared_error'\n",
    "              metrics=['accuracy', 'mean_squared_error'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "plotlosses = PlotLossesKeras()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": "<Figure size 864x864 with 3 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy\n\tval_accuracy     \t (min:    0.916, max:    0.929, cur:    0.925)\n\taccuracy         \t (min:    0.884, max:    0.930, cur:    0.930)\nloss\n\tval_loss         \t (min:    0.282, max:    0.301, cur:    0.296)\n\tloss             \t (min:    0.279, max:    0.434, cur:    0.279)\nmean_squared_error\n\tmean_squared_error \t (min:    0.011, max:    0.019, cur:    0.011)\n\tval_mean_squared_error \t (min:    0.011, max:    0.013, cur:    0.011)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<keras.callbacks.callbacks.History at 0x7f6dd26dccc0>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, Y_train,\n",
    "          epochs=10,\n",
    "          validation_data=(X_test, Y_test),\n",
    "          callbacks=[plotlosses],\n",
    "          verbose=False)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "source": [],
    "metadata": {
     "collapsed": false
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}